First the data was prepared and made clean by, 1. by joining all dataframes into one 2. renaming columns 3. removing unecessary and invalid observations (including descriptions and rows of data that are all zero)
First the data was prepared and made clean by:
1. by joining all dataframes into one
2. renaming columns
3. removing unecessary and invalid observations (including descriptions and rows of data that are all zero)
Some of the key functions that were used include full_join, mutate, rename, select) Following this, each row corresponds to an observation, each column corresponds to a variable and each cell is a value. Could insert picture of ‘clean’ data - lecture style.